AI Echocardiographic Screening of Cardiac Amyloidosis
Public ClinicalTrials.gov record NCT06664866. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Official title
Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and accurately assess common measurements made in clinical practice. Echocardiography is the most common form of cardiac imaging and is routinely and frequently used for diagnosis. However, there is often subjectivity and heterogeneity in interpretation. Artificial intelligence (AI)'s ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease. Cardiac amyloidosis (CA) is a rare, underdiagnosed disease with targeted therapies that reduce morbidity and increase life expectancy. However, CA is frequently overlooked and confused with heart failure with preserved ejection fraction. Some estimates suggest that CA can be as prevalence as 1% in a general population, with even higher prevalence in patients with left ventricular hypertrophy, heart failure, and other cardiac symptoms that might prompt echocardiography. AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis.
Study identification
- NCT ID
- NCT06664866
- Recruitment status
- Enrolling by invitation
- Study type
- Interventional
- Phase
- Not applicable
- Enrollment
- 500 participants
Conditions and interventions
Conditions
Eligibility (public fields only)
- Age range
- 22 Years and older
- Sex
- All
- Healthy volunteers
- Healthy volunteers not accepted
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Oct 27, 2024
- Primary completion
- Oct 31, 2026
- Completion
- Oct 31, 2027
- Last update posted
- Jul 21, 2026
2024 – 2027
United States locations
- U.S. sites
- 4
- U.S. states
- 3
- U.S. cities
- 4
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| Cedars Sinai Medical Center | Los Angeles | California | 90034 | — |
| Palo Alto Veteran Affairs Hospital | Palo Alto | California | 94304 | — |
| Northwestern Medicine | Chicago | Illinois | 60190 | — |
| Providence Heart and Vascular Institute | Portland | Oregon | 97225 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT06664866, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Jul 21, 2026 · Synced Sep 3, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT06664866 live on ClinicalTrials.gov.